Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5:144-152; 171-184.

Slides:



Advertisements
Similar presentations
Environmental Remote Sensing GEOG 2021
Advertisements

November 12, 2013Computer Vision Lecture 12: Texture 1Signature Another popular method of representing shape is called the signature. In order to compute.
Spatial Filtering (Chapter 3)
Topic 6 - Image Filtering - I DIGITAL IMAGE PROCESSING Course 3624 Department of Physics and Astronomy Professor Bob Warwick.
Image Processing Lecture 4
CS & CS Multimedia Processing Lecture 2. Intensity Transformation and Spatial Filtering Spring 2009.
Spatial Filtering.
Chapter 3 Image Enhancement in the Spatial Domain.
Lecture 6 Sharpening Filters
EDGE DETECTION ARCHANA IYER AADHAR AUTHENTICATION.
CS 4487/9587 Algorithms for Image Analysis
Digital Image Processing
Digital Image Processing
Digital Image Processing In The Name Of God Digital Image Processing Lecture3: Image enhancement M. Ghelich Oghli By: M. Ghelich Oghli
1Ellen L. Walker Edges Humans easily understand “line drawings” as pictures.
6/9/2015Digital Image Processing1. 2 Example Histogram.
Image Filtering CS485/685 Computer Vision Prof. George Bebis.
More Raster and Surface Analysis in Spatial Analyst
Digital Image Processing
MSU CSE 803 Stockman Linear Operations Using Masks Masks are patterns used to define the weights used in averaging the neighbors of a pixel to compute.
Edge Detection Phil Mlsna, Ph.D. Dept. of Electrical Engineering
Image Enhancement.
Image Analysis Preprocessing Arithmetic and Logic Operations Spatial Filters Image Quantization.
Lecture 2. Intensity Transformation and Spatial Filtering
MSU CSE 803 Linear Operations Using Masks Masks are patterns used to define the weights used in averaging the neighbors of a pixel to compute some result.
Computational Photography: Image Processing Jinxiang Chai.
Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Lecture 14: More Raster and Surface Analysis in Spatial Analyst Using.
Radiometric Correction and Image Enhancement
Introduction to Image Processing Grass Sky Tree ? ? Review.
Neighborhood Operations
Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5.
09 March 1999 Digital Image Processing II 1. Single-Band Image Processing Histogram Image contrast enhancement (Linear stretch, histogram equalization)
Spatial Filtering: Basics
University of Texas at Austin CS384G - Computer Graphics Fall 2010 Don Fussell Image processing.
Digital Image Processing Lecture 5: Neighborhood Processing: Spatial Filtering Prof. Charlene Tsai.
Chapter 5: Neighborhood Processing
Spatial Filtering.
COMP322/S2000/L171 Robot Vision System Major Phases in Robot Vision Systems: A. Data (image) acquisition –Illumination, i.e. lighting consideration –Lenses,
Digital Image Processing Lecture 16: Segmentation: Detection of Discontinuities Prof. Charlene Tsai.
Edge Detection and Geometric Primitive Extraction Jinxiang Chai.
Spatial-based Enhancements Lecture 5 prepared by R. Lathrop 10/99 updated 2/05 ERDAS Field Guide 5th Ed. Ch 5:
Digital Image Processing Lecture 5: Neighborhood Processing: Spatial Filtering March 9, 2004 Prof. Charlene Tsai.
Remote Sensing Image Enhancement. Image Enhancement ► Increases distinction between features in a scene ► Single image manipulation ► Multi-image manipulation.
Digital Image Processing Lecture 16: Segmentation: Detection of Discontinuities May 2, 2005 Prof. Charlene Tsai.
Course 5 Edge Detection. Image Features: local, meaningful, detectable parts of an image. edge corner texture … Edges: Edges points, or simply edges,
Machine Vision Edge Detection Techniques ENT 273 Lecture 6 Hema C.R.
Digital Filters. What are they?  Local operation (neighborhood operation in GIS terminology) by mask, window, or kernel (different words for the same.
Sharpening Spatial Filters ( high pass)  Previously we have looked at smoothing filters which remove fine detail  Sharpening spatial filters seek to.
Digital Image Processing CSC331
Image Enhancement Band Ratio Linear Contrast Enhancement
Spatial Filtering (Chapter 3) CS474/674 - Prof. Bebis.
Image Enhancement in the Spatial Domain.
Environmental Remote Sensing GEOG 2021
Spatial Image Enhancement
Image Subtraction Mask mode radiography h(x,y) is the mask.
Edge Detection Phil Mlsna, Ph.D. Dept. of Electrical Engineering Northern Arizona University.
REMOTE SENSING Digital Image Processing Radiometric Enhancement Geometric Enhancement Reference: Chapters 4 and 5, Remote Sensing Digital Image Analysis.
Digital Image Processing Lecture 16: Segmentation: Detection of Discontinuities Prof. Charlene Tsai.
Digital Image Processing
Computer Vision Lecture 9: Edge Detection II
Computer Vision Lecture 16: Texture II
Image Enhancement in the Spatial Domain
Fundamentals of Spatial Filtering
Lecture 3 (2.5.07) Image Enhancement in Spatial Domain
9th Lecture - Image Filters
Spatial operations and transformations
Linear Operations Using Masks
Digital Filters.
Image Enhancement in the Spatial Domain
Spatial operations and transformations
Presentation transcript:

Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5: ;

Spatial frequency Spatial frequency is the number of changes in brightness value per unit distance in any part of an image low frequency - tonally smooth, gradual changes high frequency - tonally rough, abrupt changes

Spatial Frequencies Zero Spatial frequencyLow Spatial frequencyHigh Spatial frequency Example from ERDAS IMAGINE Field Guide, 5th ed.

Spatial vs. Spectral Enhancement Spatial-based Enhancement modifies a pixel’s values based on the values of the surrounding pixels (local operator) Spectral-based Enhancement modifies a pixel’s values based solely on the pixel’s values (point operator)

Moving Window concept Kernel scans across row, then down a row and across again, and so on.

Focal Analysis Mathematical calculation of pixel DN values within moving window Mean, Median, Std Dev., Majority Focal value written to center pixel in moving window

Example: noise filtering

Texture Texture: variation in BV’s in a local region, gives estimate of local variability. Can be used as another layer of data in classification/ interpretation process. 1st order statistics: range, variance, std dev Window size will affect results

Texture: variance 3x3 texture7x7 texture

Pixel Convolution BV = int [ SUM i->q (SUM j->q f ij d ij ) ] F where i = row locationj = column location f ij = the coefficient of a convolution kernel at position i, j d ij = the BV of the original data at position i, j q = the dimension of the kernel, assuming a square kernel F = either the sum of the coefficients of the kernel or 1 if the sum of coefficients is zero BV = output pixel value

Example: kernel convolution Example from ERDAS IMAGINE Field Guide, 5th ed. Convolution Kernel

Example: kernel convolution Kernel: Original: X Result = 11 J=1j=2j=3 I=1 (-1)(8) +(-1)(6)+(-1)(6)= = -20 I=2 (-1)(2) +(16)(8)+(-1)(6)= = 120 I=3 (-1)(2) +(-1)(2)+(-1)(8)= = -12 F = = 8 Sum = 88 output BV = 88 / 8 = 11

Example: kernel convolution InputOutput Edge

Low vs. high spatial frequency enhancements Low frequency enhancers (low pass filters): Emphasize general trends, smooth image High frequency enhancers (high pass filters): Emphasize local detail, highlight edges

Example: Low Frequency Enhancement Kernel: Original: Output: Original: Output: Low value surrounded by higher values High value surrounded by lower values From ERDAS Field Guide p.111

Low pass filter Orignal IKONOS pan 7x7 low pass

Example: High Frequency Enhancement Kernel: Original: Output: Original: Output: Low value surrounded by higher values High value surrounded by lower values From ERDAS Field Guide p.111

High Pass filter 3x3 high pass 3x3 edge enhance

Edge detection Edge detection process: Smooth out areas of low spatial frequency and highlight edges (local changes) only 1) calculating spatial derivatives (differencing) 2) edge detecting template (Zero-sum kernels): - directional (compass templates) - non-directional (Laplacian) 3) subtracting a smoothed image from the original

Linear Edge Detection techniques Directional gradient filters produce output images whose BVs are proportional to the difference between neighboring pixel BVs in a given directional, i.e. they calculate the directional gradient Spatial differencing: Vertical: BV i,j = BV i,j - BV i,j+1 + K Horizontal: BV i,j = BV i,j - Bv i-1,j + K constant K added to make output positive

Zero sum kernels Zero sum kernels: the sum of all coefficients in the kernel equals zero. In this case, F is set = 1 since division by zero is impossible zero in areas where all input values are equal low in areas of low spatial frequency extreme in areas of high spatial frequency (high values become higher, low values lower)

Example: Linear Edge Detecting Templates Vertical:-1 0 1Horizontal: Diagonal (NW-SE): 0 1 1(NE-SW): Example: vertical template convolution Original: Output:

Linear Edge Detection Horizontal Edge Vertical Edge

Linear Line Detecting Templates Line features (i.e. rivers and roads) can be detected as pairs of edges if they are more than one pixel wide (using linear edge detection templates). If they are a single pixel wide, they can be detected using these templates: Vertical: Horizontal:

Example: Linear Line Detecting Templates Vertical: Horizontal:

Linear Line Detection Horizontal Edge Vertical Edge

Compass gradient masks Produce a maximum output for vertical (or horizontal) brightness value changes from the specified direction. For example a North compass gradient mask enhances changes that increase in a northerly direction, i.e. from south to north: North:

Example: Compass gradient masks North:1 1 1South: Example: North vs. south gradient mask NorthSouth Original: Output:...Output:

Non-directional Edge Enhancement Laplacian is a second derivative and is insensitive to direction. Laplacian highlights points, lines and edges in the image and suppresses uniform, smoothly varying regions

Nonlinear Edge Detection Sobel edge detector: a nonlinear combination of pixels Sobel = SQRT(X 2 + Y 2) X:-1 0 1Y:

Nondirectional edge filter Laplacian filterSobel filter

Edge enhancement Edge enhancement process: First detect the edges Add or subtract the edges back into the original image to increase contrast in the vicinity of the edge

Original IKONOS pan Edge enhancement Laplacian - Original – edge = edge enhanced

Original IKONOS pan Unsharp masking to enhance detail 7x7 low - Original – low pass = edge enhanced

Edge Mapping BV thresholding of the edge detector output to create a binary map of edges vs. non- edges Threshold too low: too many isolated pixels classified as edges and edge boundaries too thick Threshold too high: boundaries will consist of thin, broken segments

Fourier Transform Fourier analysis is a mathematical technique for separating an image into its various spatial frequency components. Can display the frequency domain to view magnitude and directional of different frequency components, can then filter out unwanted components and back-transform to image space. Global rather than local operator Useful for noise removal

Fourier Analysis Example Side scan sonar image of sea bottom Fourier spectrum

Fourier Analysis Example Fourier spectrum Low frequencies towards center High frequencies towards edges Image noise often shows as thin line, oriented perpendicular to original image

Fourier Analysis Example Low pass filter Back transformed image

Fourier Analysis Example Wedge filterBack transformed image